Follow
Oana-Maria Camburu
Oana-Maria Camburu
Senior Research Fellow, University College London
Verified email at ucl.ac.uk
Title
Cited by
Cited by
Year
Generation and comprehension of unambiguous object descriptions
J Mao, J Huang, A Toshev, O Camburu, AL Yuille, K Murphy
CVPR 2016, 11-20, 2016
11292016
e-SNLI: Natural Language Inference with Natural Language Explanations
OM Camburu, T Rocktäschel, T Lukasiewicz, P Blunsom
Advances in Neural Information Processing Systems (NeurIPS) 2018, 9539-9549, 2018
5132018
A Surprisingly Robust Trick for Winograd Schema Challenge
V Kocijan, AM Cretu, OM Camburu, Y Yordanov, T Lukasiewicz
ACL 2019, 2019
1162019
Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations
OM Camburu, B Shillingford, P Minervini, T Lukasiewicz, P Blunsom
ACL, 2020, 2019
832019
e-vil: A dataset and benchmark for natural language explanations in vision-language tasks
M Kayser, OM Camburu, L Salewski, C Emde, V Do, Z Akata, ...
ICCV 2021, 1244-1254, 2021
782021
Can I Trust the Explainer? Verifying Post-Hoc Explanatory Methods
OM Camburu, E Giunchiglia, J Foerster, T Lukasiewicz, P Blunsom
NeurIPS 2019 Workshop on Safety and Robustness in Decision Making, Vancouver …, 2019
64*2019
Explaining Deep Neural Networks
OM Camburu
PhD Thesis, University of Oxford, 2020
422020
Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations
BP Majumder, O Camburu, T Lukasiewicz, J Mcauley
International Conference on Machine Learning (ICML 2022), 14786-14801, 2022
41*2022
e-SNLI-VE-2.0: Corrected Visual-Textual Entailment with Natural Language Explanations
V Do, OM Camburu, Z Akata, T Lukasiewicz
IEEE CVPR Workshop on Fair, Data Efficient and Trusted Computer Vision, 2020, 2020
372020
Learning from the Best: Rationalizing Prediction by Adversarial Information Calibration
L Sha, OM Camburu, T Lukasiewicz
AAAI 2021, 2020
342020
WikiCREM: A Large Unsupervised Corpus for Coreference Resolution
V Kocijan, OM Camburu, AM Cretu, Y Yordanov, P Blunsom, ...
EMNLP 2019, 2019
292019
Faithfulness Tests for Natural Language Explanations
P Atanasova, OM Camburu, C Lioma, T Lukasiewicz, JG Simonsen, ...
ACL 2023, 2023
202023
The Struggles of Feature-Based Explanations: Shapley Values vs. Minimal Sufficient Subsets
OM Camburu, E Giunchiglia, J Foerster, T Lukasiewicz, P Blunsom
AAAI Explainable Agency in Artificial Intelligence Workshop 2021, 2020
182020
Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations
Y Yordanov, V Kocijan, T Lukasiewicz, OM Camburu
Findings of EMNLP 2022, 2021
15*2021
Explaining Chest X-ray Pathologies in Natural Language
M Kayser, C Emde, OM Camburu, G Parsons, B Papiez, T Lukasiewicz
MICCAI 2022, 2022
142022
Cyclotomic coefficients: gaps and jumps
OM Camburu, EA Ciolan, F Luca, P Moree, IE Shparlinski
Journal of Number Theory 163, 211-237, 2016
122016
The Gap on GAP: Tackling the Problem of Differing Data Distributions in Bias-Measuring Datasets
V Kocijan, OM Camburu, T Lukasiewicz
AAAI 2021, 2020
112020
Towards explainable and trustworthy autonomous physical systems
D Omeiza, S Anjomshoae, K Kollnig, OM Camburu, K Främling, L Kunze
Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing …, 2021
62021
KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations
M Jang, BP Majumder, J McAuley, T Lukasiewicz, OM Camburu
ACL 2023, 2023
42023
Logical reasoning for natural language inference using generated facts as atoms
J Stacey, P Minervini, H Dubossarsky, OM Camburu, M Rei
arXiv preprint arXiv:2305.13214, 2023
42023
The system can't perform the operation now. Try again later.
Articles 1–20